SoroushVahidi/scidocs huggingface dataset:
Thunderstrike: EFI firmware bootkits for Apple MacBooks
(2015)

There are several flaws in Apple's MacBook firmware security that allows untrusted modifications to be written to the SPI Flash boot ROM of these laptops. This capability represents a new class of persistent firmware rootkits, or 'bootkits', for the popular Apple MacBook product line. Stealthy bootkits can conceal themselves from detection and prevent software attempts to remove them. Malicious modifications to the boot ROM are able to survive re-installation of the operating system and even hard-drive replacement. Additionally, the malware can install a copy of itself onto other Thunderbolt devices' Option ROMs as a means to spread virally across air-gap security perimeters. Apple has fixed some of these flaws as part of CVE 2014-4498, but there is no easy solution to this class of vulnerability, since the MacBook lacks trusted hardware to perform cryptographic validation of the firmware at boot time.
High-Q MEMS Resonators for Laser Beam Scanning Displays
(2012)

This paper reports on design, fabrication and characterization of high-Q MEMS resonators to be used in optical applications like laser displays and LIDAR range sensors. Stacked vertical comb drives for electrostatic actuation of single-axis scanners and biaxial MEMS mirrors were realized in a dual layer polysilicon SOI process. High Q-factors up to 145,000 have been achieved applying wafer level vacuum packaging technology including deposition of titanium thin film getters. The effective reduction of gas damping allows the MEMS actuator to achieve large amplitudes at high oscillation frequencies while driving voltage and power consumption can be minimized. Exemplarily shown is a micro scanner that achieves a total optical scan angle of 86 degrees at a resonant frequency of 30.8 kHz, which fulfills the requirements for HD720 resolution. Furthermore, results of a new wafer based glass-forming technology for fabrication of three dimensionally shaped glass lids with tilted optical windows are presented.
Supplementary material for the paper : Discriminative Correlation Filter with Channel and Spatial Reliability DCF-CSR
(2017)

This is the supplementary material for the paper ”Discriminative Correlation Filter with Channel and Spatial Reliability” submitted to the CVPR 2017. Due to spatial constraints, parts not crucial for understanding the DCF-CSR tracker formulation, but helpful for gaining insights, were moved here. 1. Derivation of the augmented Lagrangian minimizer This section provides the complete derivation of the closed-form solutions for the relations (9,10) in the submitted paper [3]. The augmented Lagrangian from Equation (5) in [3] is L(ĥc,h, l̂) = ‖ĥc diag(f̂)− ĝ‖ + λ 2 ‖hm‖ + (1) [̂l(ĥc − ĥm) + l̂(ĥc − ĥm)] + μ‖ĥc − ĥm‖, with hm = (m h). For the purposes of derivation we will rewrite (1) into a fully vectorized form L(ĥc,h, l̂) = ‖ĥc diag(f̂)− ĝ‖ + λ 2 ‖hm‖+ (2) [̂ l(ĥc − √ DFMh) + l̂(ĥc − √ DFMh) ] + μ‖ĥc − √ DFMh‖, where F denotes D × D orthonormal matrix of Fourier coefficients, such that the Fourier transform is defined as x̂ = F(x) = √ DFx and M = diag(m). For clearer representation we denote the four terms in the summation (2) as L(ĥc,h, l̂) = L1 + L2 + L3 + L4, (3) where L1 = ( ĥc diag(f̂)− ĝ )( ĥc diag(f̂)− ĝ )T , (4)
Efficient Energy-Optimal Routing for Electric Vehicles
(2011)

Traditionally routing has focused on finding shortest paths in networks with positive, static edge costs representing the distance between two nodes. Energy-optimal routing for electric vehicles creates novel algorithmic challenges, as simply understanding edge costs as energy values and applying standard algorithms does not work. First, edge costs can be negative due to recuperation, excluding Dijkstra-like algorithms. Second, edge costs may depend on parameters such as vehicle weight only known at query time, ruling out existing preprocessing techniques. Third, considering battery capacity limitations implies that the cost of a path is no longer just the sum of its edge costs. This paper shows how these challenges can be met within the framework of A* search. We show how the specific domain gives rise to a consistent heuristic function yielding an O(n) routing algorithm. Moreover, we show how battery constraints can be treated by dynamically adapting edge costs and hence can be handled in the same way as parameters given at query time, without increasing run-time complexity. Experimental results with real road networks and vehicle data demonstrate the advantages of our solution.
Matching RGB Images to CAD Models for Object Pose Estimation
(2018)

We propose a novel method for 3D object pose estimation in RGB images, which does not require pose annotations of objects in images in the training stage. We tackle the pose estimation problem by learning how to establish correspondences between RGB images and rendered depth images of CAD models. During training, our approach only requires textureless CAD models and aligned RGB-D frames of a subset of object instances, without explicitly requiring pose annotations for the RGB images. We employ a deep quadruplet convolutional neural network for joint learning of suitable keypoints and their associated descriptors in pairs of rendered depth images which can be matched across modalities with aligned RGB-D views. During testing, keypoints are extracted from a query RGB image and matched to keypoints extracted from rendered depth images, followed by establishing 2D-3D correspondences. The object’s pose is then estimated using the RANSAC and PnP algorithms. We conduct experiments on the recently introduced Pix3D [33] dataset and demonstrate the efficacy of our proposed approach in object pose estimation as well as generalization to object instances not seen during training.
Field-weakening Control Algorithm for Interior Permanent Magnet Synchronous Motor Based on Space-Vector Modulation Technique
(2013)

We investigate the implementation of field-weakening control for interior permanent magnet synchronous motor (IPMSM), and analyze the algorithm of field-weakening control in d-q axes. To deal with the problem that the dc-link voltage is not fully utilized when the voltage regulation is employed, we propose a new field-weakening scheme based on the space-vector modulation technique. The duty-time of zero-voltage vector is used as the feedback signal to decide the switching of fieldweakening. To avoid the regulation lag in the q-axis component, we apply the lead-angle control during the field-weakening progress. The proposed scheme is validated with Matlab/Simulink tool. Simulation results show that the scheme is feasible. It not only improves the utilization ratio of the space-vector-pulse-width-modulation (SVPWM) inverter, but also achieves a smooth transition between the constant-torque mode below the base-speed and the constant-power mode above the basespeed.
An Image Matching Algorithm Based on SIFT and Improved LTP
(2013)

SIFT is one of the most robust and widely used image matching algorithms based on local features. But the key-points descriptor of SIFT algorithm have 128 dimensions. Aiming to the problem of its high dimension and complexity, a novel image matching algorithm is proposed. The descriptors of SIFT key-points are constructed by the rotation invariant LTP, city-block distance is also employed to reduce calculation of key-points matching. The experiment is achieved through different lighting, blur changes and rotation of images, the results show that this method can reduce the processing time and raise image matching efficiency.
An Over-110-GHz-Bandwidth 2:1 Analog Multiplexer in 0.25-μm InP DHBT Technology
(2018)

This paper presents an over-110-GHz-bandwidth 2:1 analog multiplexer (AMUX) for ultra-broadband digital-to-analog (D/A) conversion subsystems. The AMUX was designed and fabricated by using newly developed $\pmb{0.25-\mu \mathrm{m}}$ -emitter-width InP double heterojunction bipolar transistors (DHBTs), which have a peak $\pmb{f_{\mathrm{T}}}$ and $\pmb{ f\displaystyle \max}$ of 460 and 480 GHz, respectively. The AMUX IC consists of lumped building blocks, including data-input linear buffers, a clock-input limiting buffer, an AMUX core, and an output linear buffer. The measured 3-dB bandwidth for data and clock paths are both over 110 GHz. In addition, it measures and obtains time-domain large-signal sampling operations of up to 180 GS/s. A 224-Gb/s (112-GBaud) four-level pulse-amplitude modulation (PAM4) signal was successfully generated by using this AMUX. To the best of our knowledge, this AMUX IC has the broadest bandwidth and the fastest sampling rate compared with any other previously reported AMUXes.
Field-stop layer optimization for 1200V FS IGBT operating at 200˚C
(2014)

This paper is concerned with design considerations for enabling the operation of Field-Stop Insulated Gate Bipolar Transistors (FS IGBTs) at 200 C. It is found that through a careful optimization of the Field-Stop layer doping profile the device has a low leakage current and delivers a favorable tradeoff between the on-state voltage (Von) and turn-off loss (Eoff). An investigation of the adverse effects of increasing the junction temperature on the temperature-dependent properties of the FS IGBTs is also discussed herein.
Theory of Outlier Ensembles
(2017)

Outlier detection is an unsupervised problem, in which labels are not available with data records [2]. As a result, it is generally more challenging to design ensemble analysis algorithms for outlier detection. In particular, methods that require the use of labels in intermediate steps of the algorithm cannot be generalized to outlier detection. For example, in the case of boosting, the classifier algorithm needs to be evaluated in the intermediate steps of the algorithm with the use of training-data labels. Such methods are generally not possible in the case of outlier analysis. As discussed in [1], there are unique reasons why ensemble analysis is generally more difficult in the case of outlier analysis as compared to classification. In spite of the unsupervised nature of outlier ensemble analysis, we show that the theoretical foundations of outlier analysis and classification are surprisingly similar. A number of useful discussions on the theory of classification ensembles may be found in [27, 29, 33]. Further explanations on the use of the bias-variance decomposition in different types of classifiers such as neural networks, support vector machines, and rule-based classifiers are discussed in [17, 30, 31]. It is noteworthy that the biasvariance decomposition is often used in customized ways for different types of base classifiers and combination methods; this general principle is also true in outlier detection. Several arguments have recently been proposed on the theory explaining the accuracy improvements of outlier ensembles. In some cases, incorrect new arguments (such as those in [32]) are proposed to justify experimental results that can be explained by well-known ideas, and an artificial distinction is made between the theory of classification ensembles and outlier ensembles. A recent paper [4]
The Usability of Open Source Software
(2003)

Twidale Open source communities have successfully developed a great deal of software although most computer users only use proprietary applications. The usability of open source software is often regarded as one reason for this limited distribution. In this paper we review the existing evidence of the usability of open source software and discuss how the characteristics of open source development influence usability. We describe how existing human-computer interaction techniques can be used to leverage distributed networked communities, of developers and users, to address issues of usability.
Detecting adversarial advertisements in the wild
(2011)

In a large online advertising system, adversaries may attempt to profit from the creation of low quality or harmful advertisements. In this paper, we present a large scale data mining effort that detects and blocks such adversarial advertisements for the benefit and safety of our users. Because both false positives and false negatives have high cost, our deployed system uses a tiered strategy combining automated and semi-automated methods to ensure reliable classification. We also employ strategies to address the challenges of learning from highly skewed data at scale, allocating the effort of human experts, leveraging domain expert knowledge, and independently assessing the effectiveness of our system.
Social coding in GitHub: transparency and collaboration in an open software repository
(2012)

Social applications on the web let users track and follow the activities of a large number of others regardless of location or affiliation. There is a potential for this transparency to radically improve collaboration and learning in complex knowledge-based activities. Based on a series of in-depth interviews with central and peripheral GitHub users, we examined the value of transparency for large-scale distributed collaborations and communities of practice. We find that people make a surprisingly rich set of social inferences from the networked activity information in GitHub, such as inferring someone else's technical goals and vision when they edit code, or guessing which of several similar projects has the best chance of thriving in the long term. Users combine these inferences into effective strategies for coordinating work, advancing technical skills and managing their reputation.
Searching for build debt: Experiences managing technical debt at Google
(2012)

With a large and rapidly changing codebase, Google software engineers are constantly paying interest on various forms of technical debt. Google engineers also make efforts to pay down that debt, whether through special Fixit days, or via dedicated teams, variously known as janitors, cultivators, or demolition experts. We describe several related efforts to measure and pay down technical debt found in Google's BUILD files and associated dead code. We address debt found in dependency specifications, unbuildable targets, and unnecessary command line flags. These efforts often expose other forms of technical debt that must first be managed.
A Fully Integrated Reconfigurable Self-Startup RF Energy-Harvesting System With Storage Capability
(2017)

This paper introduces a fully integrated RF energy-harvesting system. The system can simultaneously deliver the current demanded by external dc loads and store the extra energy in external capacitors, during periods of extra output power. The design is fabricated in 0.18- $\mu \text{m}$  CMOS technology, and the active chip area is 1.08 mm2. The proposed self-startup system is reconfigurable with an integrated LC matching network, an RF rectifier, and a power management/controller unit, which consumes 66–157 nW. The required clock generation and the voltage reference circuit are integrated on the same chip. Duty cycle control is used to operate for the low input power that cannot provide the demanded output power. Moreover, the number of stages of the RF rectifier is reconfigurable to increase the efficiency of the available output power. For high available power, a secondary path is activated to charge an external energy storage element. The measured RF input power sensitivity is −14.8 dBm at a 1-V dc output.
HDTLR: A CNN based Hierarchical Detector for Traffic Lights
(2018)

Reliable traffic light detection is one crucial key component for autonomous driving in urban areas. This includes the extraction of direction arrows contained within the traffic lights as an autonomous car will need this information for selecting the traffic light corresponding to its current lane. Current state of the art traffic light detection systems are not able to provide such information. Within this work we present a hierarchical traffic light detection algorithm, which is able to detect traffic lights and determine their state and contained direction information within one CNN forward pass. This Hierarchical DeepTLR (HDTLR) outperforms current state of the art traffic light detection algorithms in state aware detection and can detect traffic lights with direction information down to a size of 4 pixel in width at a frequency of 12 frames per second.
Implementation of behavior change techniques in mobile applications for physical activity.
(2015)

BACKGROUND
Mobile applications (apps) for physical activity are popular and hold promise for promoting behavior change and reducing non-communicable disease risk. App marketing materials describe a limited number of behavior change techniques (BCTs), but apps may include unmarketed BCTs, which are important as well.


PURPOSE
To characterize the extent to which BCTs have been implemented in apps from a systematic user inspection of apps.


METHODS
Top-ranked physical activity apps (N=100) were identified in November 2013 and analyzed in 2014. BCTs were coded using a contemporary taxonomy following a user inspection of apps.


RESULTS
Users identified an average of 6.6 BCTs per app and most BCTs in the taxonomy were not represented in any apps. The most common BCTs involved providing social support, information about others' approval, instructions on how to perform a behavior, demonstrations of the behavior, and feedback on the behavior. A latent class analysis of BCT configurations revealed that apps focused on providing support and feedback as well as support and education.


CONCLUSIONS
Contemporary physical activity apps have implemented a limited number of BCTs and have favored BCTs with a modest evidence base over others with more established evidence of efficacy (e.g., social media integration for providing social support versus active self-monitoring by users). Social support is a ubiquitous feature of contemporary physical activity apps and differences between apps lie primarily in whether the limited BCTs provide education or feedback about physical activity.
Future affective technology for autism and emotion communication.
(2009)

People on the autism spectrum often experience states of emotional or cognitive overload that pose challenges to their interests in learning and communicating. Measurements taken from home and school environments show that extreme overload experienced internally, measured as autonomic nervous system (ANS) activation, may not be visible externally: a person can have a resting heart rate twice the level of non-autistic peers, while outwardly appearing calm and relaxed. The chasm between what is happening on the inside and what is seen on the outside, coupled with challenges in speaking and being pushed to perform, is a recipe for a meltdown that may seem to come 'out of the blue', but in fact may have been steadily building. Because ANS activation both influences and is influenced by efforts to process sensory information, interact socially, initiate motor activity, produce meaningful speech and more, deciphering the dynamics of ANS states is important for understanding and helping people on the autism spectrum. This paper highlights advances in technology that can comfortably sense and communicate ANS arousal in daily life, allowing new kinds of investigations to inform the science of autism while also providing personalized feedback to help individuals who participate in the research.
Toward Massive Machine Type Cellular Communications
(2017)

Cellular networks have been engineered and optimized to carrying ever-increasing amounts of mobile data, but over the last few years, a new class of applications based on machine-centric communications has begun to emerge. Automated devices such as sensors, tracking devices, and meters, often referred to as machine-to-machine (M2M) or machine-type communications (MTC), introduce an attractive revenue stream for mobile network operators, if a massive number of them can be efficiently supported. The novel technical challenges posed by MTC applications include increased overhead and control signaling as well as diverse application-specific constraints such as ultra-low complexity, extreme energy efficiency, critical timing, and continuous data intensive uploading. This article explains the new requirements and challenges that large-scale MTC applications introduce, and provides a survey of key techniques for overcoming them. We focus on the potential of 4.5G and 5G networks to serve both the high data rate needs of conventional human-type communication (HTC) subscribers and the forecasted billions of new MTC devices. We also opine on attractive economic models that will enable this new class of cellular subscribers to grow to its full potential.
Partially Observable Reinforcement Learning for Intelligent Transportation Systems
(2018)

Intelligent Transportation Systems (ITS) have attracted the attention of researchers and the general public alike as a means to alleviate traffic congestion. Recently, the maturity of wireless technology has enabled a cost-efficient way to achieve ITS by detecting vehicles using Vehicle to Infrastructure (V2I) communications. Traditional ITS algorithms, in most cases, assume that every vehicle is observed, such as by a camera or a loop detector, but a V2I implementation would detect only those vehicles with wireless communications capability. We examine a family of transportation systems, which we will refer to as ‘Partially Detected Intelligent Transportation Systems’. An algorithm that can act well under a small detection rate is highly desirable due to gradual penetration rates of the underlying wireless technologies such as Dedicated Short Range Communications (DSRC) technology. Artificial Intelligence (AI) techniques for Reinforcement Learning (RL) are suitable tools for finding such an algorithm due to utilizing varied inputs and not requiring explicit analytic understanding or modeling of the underlying system dynamics. In this paper, we report a RL algorithm for partially observable ITS based on DSRC. The performance of this system is studied under different car flows, detection rates, and topologies of the road network. Our system is able to efficiently reduce the average waiting time of vehicles at an intersection, even with a low detection rate.
Fuzzy & Datamining based Disease Prediction Using K-NN Algorithm
(2014)

Disease diagnosis is one of the most important applications of such system as it is one of the leading causes of deaths all over the world. Almost all system predicting disease use inputs from complex tests conducted in labs and none of the system predicts disease based on the risk factors such as tobacco smoking, alcohol intake, age, family history, diabetes, hypertension, high cholesterol, physical inactivity, obesity. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease. K-Nearest-Neighbour (KNN) is one of the successful data mining techniques used in classification problems. However, it is less used in the diagnosis of heart disease patients. Recently, researchers are showing that combining different classifiers through voting is outperforming other single classifiers. This paper investigates applying KNN to help healthcare professionals in the diagnosis of disease specially heart disease. It also investigates if integrating voting with KNN can enhance its accuracy in the diagnosis of heart disease patients. The results show that applying KNN could achieve higher accuracy than neural network ensemble in the diagnosis of heart disease patients. The results also show that applying voting could not enhance the KNN accuracy in the diagnosis of heart disease.